The ultimate proof of our understanding of biological or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered and natural systems toward a desired state, we lack a framework to control complex self-organized systems. Here I will explore the controllability of an arbitrary complex network, identifying the set of driver nodes whose time-dependent control can guide the system’s entire dynamics. Virtually all technological and biological networks must be able to control their internal processes. Given that, issues related to control deeply shape the topology and the vulnerability of real systems. Consequently, unveiling the control principles of real networks, the goal of our research, forces us to address series of fundamental questions pertaining to our understanding of complex systems. Finally, I will discuss how control principles inform our ability to predict neurons involved in specific processes in the brain, offering an avenue to experimentally falsify and test the predictions of network control.